Chad - trigger development

Analysis last updated 2022-01-03


For Chad an OCHA anticipatory action framework is being developed to anticipate drought. Such a framework consists of activities, financing, and a trigger. This document summarizes the work done by the Centre for Humanitarian Data (CHD), whose work focuses on the development of the trigger. The development of the trigger in Chad has been a collaborative process for which a preliminary trigger was proposed by a technical working group consisting of several in-country organizations. The validation and fine-tuning of this trigger was thereafter executed by CHD. This document is a summary, more elaborate analyses and code can be found on GitHub.

Context

Chad has several climatological zones. For this pilot the focus is on a part of the Sahelian zone, see the map below, as this area was identified as most vulnerable to drought. More specifically the ADMIN1 areas of Lac, Kanem, Barh-El-Gazel, Batha, and Wadi Fira are the focus. For the trigger it was decided to also only include this area of interest within the analyses, i.e. we look at the drought within the area and not at e.g. a possible decreased trade due to drought in other areas.

The focus of the trigger is on the rainy season. In Chad the rainy season is from June to September, with most rainfall in July and August. Below the historical median rainfall per month is shown. The average monthly rainfall over all the whole area and all years is 14mm in June, 61mm in July, 89mm in August, and 29mm in September.

Preliminary trigger

The proposed preliminary trigger is as follows:

Proposed preliminary trigger

Proposed preliminary trigger

Trigger validation

Based on the prelimanry trigger we did a validation and fine-tuning. This process consisted of four steps: 1) compiling a list of historical socio-economic drought years 2) extract the observed values of the trigger indicators at the end of the rainy season and see how these correlate with the list of historical drought years 3) extract the date of the same trigger indicators but that is forecasted or available during the rainy season and see how these correlate with the observed end of season data. 4) if no strong correlation in 2) or 3) test correlations with the same indicators but at different thresholds.

Historical socio-economic drought

With anticipatory action we want to anticipate droughts that cause an increase in humanitarian needs. Such droughts are often referred to as socio-economic droughts. To validate the trigger we can thus look at the past and see how well the moments the trigger is reached correspond with historical years we would have wanted to be triggering. To do so we thus need a list of historical socio-economic droughts. This kind of impact data is not directly available. It is scattered across sources and often has no detailed information on the temporal and spatial scale.

Nevertheless, we attempted to compile a list of worst drought years. We solely focus on the year, so not specific months, and look at Chad in general so not at specific areas as this data is often not available. The compiled list is by no means fully validated and shouldn’t be seen as a ground-truth dataset. We therefore use the list for part of the analyses but don’t base all decisions on it. The list was compiled in discussion with CERF colleagues and based on several sources.

The first source we can look at are the historical CERF allocations, which are available since 2006. In Chad drought-related allocations were in 2010-01, 2011-12, and 2018-05. By analyzing the linked documents we can conclude that these allocations were in response to poor rainy seasons during 2009, 2011, and 2017.

Another source is FewsNet which maps the food insecurity situation since 2009. In their reports the causes of food insecurity are often mentioned and thus we can use these reports to look for a mention of meteorological drought. We scanned these reports manually for the years there were CERF allocations. The 2010 report indicates increased food insecurity caused by erratic patterns during the 2009 rainy season. In 2011 a shorter than usual rainy season is mentioned. 2017 mentions localized dry spells causing bad pastoral conditions. This thus matches the allocations.

Another source is the official drought declarations. These are known to be in 2005/2006, 2009/2010, 2016/2017 but we don’t have any source of these. It thus remains unclear to which rainy seasons they relate. [What to do with them?! Especially the 2005 one we havent seen anywhere else]

Lastly, we can inspect other sources of literature such as the African Risk Capacity 2016/17 operations plan, the 2017 multi-risk contigency plan and the worldbank statistics. [we have the documents of the first two but couldn’t find them online]

ARC reports 1968 to 1973, 1983, 1984, 2004, and 2009 as drought years.

The multi-risk contigency plan reports the years 1969, 1981, 1993, 1997, 2001, 2009, and 2012. The world bank reports the years 1993, 1997, 2001, 2009, 2012, 2017. These three sources all don’t provide more information on the cause of the drought and it remains unclear whether these years refer to the rainy season or the lean season.

All in all, we can see that the different sources show similarities but also differences. Moreover, with the reports of FewsNet and CERF we see that socio-economic drought can be caused by different types of meteorological drought, e.g. overall deficitis of rain, erratic temporal and spatial pattterns, and dry spells.

We take the union of the different sources. With the exception of 2012 as we assume this refers to the drought observed in 2011. We thus get as drought years list: 1968-1973, 1983, 1984, 1993, 1997, 2001, 2004, 2005, 2009, 2011, and 2017. Note that three of the six sources only include data after 2004.

[We could further dive into the FN reports.. basically now we only checked the years for which there were CERF allocations] [what to do with the drought declarations? I am not convinced by the 2005 year.. might be caused by 2004? So far we have left it out, but not sure how to handle it ] [Make a table with the different sources? ]

Indicator data sources

Observed end-of-season indicators

We have 7 proposed trigger indicator values: below average rainfall, late onset, long dry stretches, end of season rainfall, WRSI and Biomass. We decided to first focus on the indicators for which we know there is wide availability of forecasts, which are below average rainfall, WRSI, and Biomass. We also added NDVI as this came up during several discussions with the team. Given the definitions, WRSI, Biomass, and NDVI are expected to have some correlation.

Below average rainfall

As data source to determine observed below average rainfall, we used CHIRPS. CHIRPS is raster data at 0.05 degree resolution and is available since 1981. We compute for each of the raster cells whether it observed below average rainfall for each 3-month period (commonly referred to as season). We then aggregated this to the area of interest, expressing the percentage of the area that observed below average rainfall. The climatological reference period to compute the terciles was 1982-2010. The graph below shows the percentage of the area of interest experiencing below average precipitation for the JJA and JAS season. The bars colored red are those indicated as drought years from our previous exploration.

From these graphs, we can conclude several things:

  • In the 80s there was a particularly dry period. This is also confirmed by other sources.
  • Since 2000 the 5 years with the largest percentage of below average rainfall were 2000, 2004, 2008, 2013, and 2015.
  • Since 2017 we haven’t seen any significant percentage of below average rainfall.
  • There is a limited correlation between below average precipitation and the list of socio-economic droughts. It is important to note here that the list of historical droughts is not fully validated and thus not too many conclusions should be drawn to it. For example the 2001 drought might refer to the 2000 rainy season. Nevertheless, for example in 2017 we know there was localized meteorological drought that are clearly not reflected in the below average seasonal precipitation.

Biomasse

Biomasse is based on a productivity measure of dry matter production (DMP) over 36 dekads in the year, measuring average daily production in each dekad (from VEGETATION sensor of PROBA-V). We use the biomasse anomaly which is the biomasse divided by the mean biomasse. Biomasse is specifically designed with pastoralist areas in mind and measures more specifically the impact to vegetation than rainfall does. The Biomasse data is made available near the end of the following dekad at ADMIN2 level. We download this data and aggregate it to the area of interest by taking the sum across the admins.

The graph below shows the Biomasse anomaly per year from June to November. We can see that

  • The anomaly significantly differs per year
  • In several seasons we see a dip earlier in the season but this recovers later, i.e. begin of season values are not always representative for the end of season state.
  • An anomaly of <= 40 as proposed by the working group is never reached

We can also compare the Biomasse values in November to the list of socio-economic droughts. Since the anomaly of 40 is never seen, we changed it to 80 as this gave the best correspondence with the historical socio-economic droughts. The confusion matrix is shown below. From this we can see that there is a decent correlation.

WRSI

The WRSI is based on the use of precipitation and potential evaporanspiration (PET) data. Currently, these are sourced from NOAA CPC rainfall estimates (RFE) and Global Data Assimilation System (GDAS) PET data. The data is made available near the beginning of the following dekad. They generate separate data for cropland and rangeland.

NDVI

[the y labels of this graph cannot be correct..]

Comparison of the data sources

Now that we have analyzed the 4 indicators individually, we can see how they correlate. If two or more indicators show a same pattern, then we prefer to simplify the trigger by not including all of them.

We can firstly do a very simple comparison by looking at the 5 worst years for each indicator since 2000, see below.

It is not only about the match of years, but also about the separability between drought and non-drought years that shows the quality of the indicator [add some of Seth’s green/red graphs? ]

Forecasted indicators

Note: naming it forecast is bit misleading

Below average precipitation

In the previous section we saw that the observed below average precipitation in JJA and JAS doesn’t correspond very well with our list of historical socio-economic droughts. Nevertheless, it was decided to inspect the use of forecast of below average precipitation as this is the only widely-used product that is available well-ahead of time. Several seasonal forecast providers exist and the proposed provider was IRI. This data is only available since 2017. Since we didn’t see any observed below average precipitation since 2017

Proposed trigger January 2022